import.q.sorts: Q methodology: import Q-sorts from CSV

In aiorazabala/qmethod: Analysis of Subjective Perspectives Using Q Methodology

Q methodology: import Q-sorts from CSV

Description

Turns raw Q-sorts (from *.CSV) into a Q-sorts array (when there are > 2 conditions) or matrix (with single condition).

Usage

import.q.sorts(q.sorts.dir,	q.set, q.distribution, 
               conditions = NULL, manual.lookup = NULL)

Arguments

q.sorts.dir	A relative path to a directory structure where: (optional) folders are (within-subjects) conditions (such as before or after), if there is more than one condition as per the conditions argument. If there are no conditions (defaults to NULL), there should be no folders. file names of *.CSV files are participant names (might be given pseudonyms). *.CSV files contain raw Q-sorts, where each line contains item IDs, such as ‘⁠,,sta12,sta64,,⁠’. All lines below the maximum Q-sort height will be ignored and can have arbitrary input. For example, in a study with a highest column of 8 cards, everything below the 8th line in the file will be ignored. There is no need to include the values of the x-axis (say, -4 to +4) in these files. If they are included, they should be the last row. cells contains either all manual or all automatic item IDs (such as "sta12"), both as produced by make.cards.
q.set	A matrix with handles as row names, languages (if applicable) in columns, as read in by build.q.set.
q.distribution	The chosen Q distribution as a vector of integers, such as c(1,3,1).
conditions	A character vector of (optional) study (within-subjects) conditions, such as c("before","after"), same as folders under q.sorts.dir. Defaults to NULL in which case there is only one condition, and *.CSV files are expected directly under q.sorts.dir.
manual.lookup	A matrix with handles (such as q-is-great, same as in build.q.set, import.q.concourse) as row names, and arbitrary strings (item IDs, such as "it212") in any of the columns as printed on the Q-cards by make.cards. Defaults to NULL in which case items are automatically identified by automatic hash IDs, as also produced by make.cards.

Details

This function imports Q-sorts from their raw format stored in *.CSV files, in the form in which they were sorted by participants (applicable to Q-sorts with forced distributions only).

Q-sorts in their raw form have columns as ranks (from, say, -6 to +6) with cards (items) sorted in rows. The vertical dimension of Q-sorts is meaningless.

Q-sorts are conveniently entered as *.CSV (comma separated values) files in standard spreadsheet editors. This function ignores any rows in the files below the maximum height of columns expected from q.distribution.

It is recommended that Q-sort data are kept in their rawest form, with clear documentation of any processing applied to this data. This is also good practice for reproducible research.

Q-sorts are best entered not by typing up the full form of an item, but some unique string (ID) printed on the card. This function, and, analogously, make.cards and import.q.feedback offer a manual and automatic way to create these IDs, which are then expected as input (see qmethod-package for details).

The automatic summary of full item wordings, technically known as hashing, proceeds internally by passing the full item wording to the digest function of the package digest (with arguments set to algo = crc32, serialize = FALSE.)

Q-sorts are conveniently entered as *.CSV (comma separated values) files in standard spreadsheet editors.

This function includes a number of tests to verify the integrity of entered Q-sorts:

1. manual.lookup tables provided are tested for duplicate identifiers.

2. Function returns a warning if some participants do not have Q-sort files under all conditions (applies only if there are more than one conditions).

3. Function errors out if there are item IDs in a Q-sort not matched by any manually or automatically specified ID, respectively (see qmethod-package for details).

4. Function errors out if the distribution in a given Q-sort does not conform to the defined q.distribution.

5. Function errors out if there are items in the sample q.set that cannot be found in any given Q-sort.

6. Function errors out if there are items in a given Q-sort that cannot be found in the sample q.set.

If you are not familiar with the terminology of item handle, ID and wording or the file structure expected for import functions, please read the respective sections in the documentation for qmethod-package first or consider the package website.

Value

Returns a matrix (when there is a single condition) or array (with two or more conditions) with handles as row names, people as column names, conditions (if more than one) as 3rd dimension and Q-sort ranks in cells, as expected for analysis by qmethod.

Notice that qmethod expects a matrix (with two dimensions). If you have several conditions, and therefore an array of data, you must pass them to qmethod in individual 'slices' of conditions, using subsetting.

Note

This function currently works only with forced distributions.

When argument manual.lookup is set to NULL, IDs are computed by "summarising" the complete item wordings ("Q Method is used by a crazy, but charming community of ...") into a hexadecimal number ("ae128fs"), a process known as cryptographic hashing. These hash values change whenever anything in the full item wordings is changed, and allow a precise identification of different versions of an item. This function never exposes users to the hash values; the encrypting and decrypting are done under the hood by the respective functions. Automatic, hashed IDs are generally recommended and easier to use, but some caveats apply.

Hashed identification has not been widely tested in Q studies and should be used with great care and only for extra convenience. When using hash identification, researchers should be careful to record the precise item wordings at the time of hashing for the printed Q-cards, preferably with a version control system. Researchers should also record the complete Q-sorts of participants in an unhashed form, such as a picture of the completed sort in full wordings, in case problems with the hashing arise.

This function does not test whether Q-sorts were entered correctly into the *.CSV files. It is recommended to enter any given Q-sort more than once and have a spreadsheet editor compare several entry attempts for consistency. This function ignores any entries in *.CSV files below the highest row expected by the q.distribution.

Author(s)

Maximilian Held

See Also

import.q.concourse, import.q.feedback, build.q.set, make.cards, qmethod

Examples

## Import a Q sample from a directory of *.CSV files
data(importexample)
path.sorts <- paste(                 # this part is only for the example!
  path.package("qmethod"),           # just to make sure, use absolute path
  # import example files are in root/extdata of package:
  "/extdata/importexample/qsorts/",  # location of sorts
  sep = ""
  )
q.sorts <- import.q.sorts(           # now import the sorts
  q.sorts.dir = path.sorts,          # add your path here
  q.set = importexample$q.set,       # as produced by build.q.set
  q.distribution = c(1,2,1),         # very simple distribution
  conditions = c("before","after"),  # enter your conditions here, same as in path
  manual.lookup = matrix(  # ideally empty for automatic hashing, 
                           # or read in from *.CSV file
    c("i01","i02","i03","i04"),
    ncol = 1,
    nrow = 4,
    dimnames = list(c("r-dominance","q-uprising","small-village",
                      "life-with-q"),"ID")
    )
  )

aiorazabala/qmethod documentation built on Nov. 23, 2023, 1:25 a.m.